scholarly journals Identification of novel cyclin gene fusion transcripts in endometrioid ovarian carcinomas

2018 ◽  
Vol 143 (6) ◽  
pp. 1379-1387 ◽  
Author(s):  
Antonio Agostini ◽  
Marta Brunetti ◽  
Ben Davidson ◽  
Claes Göran Tropé ◽  
Sverre Heim ◽  
...  
Keyword(s):  
Gene Fusion ◽  
Ovarian Carcinomas ◽  
Fusion Transcripts ◽  
Cyclin Gene

NPM/ALK gene fusion transcripts identify a distinct subgroup of null type Ki‐1 positive anaplastic large cell lymphomas

1996 ◽  
Vol 92 (4) ◽  
pp. 866-871 ◽  
Author(s):  
Ingrid Simonitsch ◽  
Eva Renate Panzer‐Gruemayer ◽  
Daniel W. Ghali ◽  
Andreas Zoubek ◽  
ThaddÄus Radaszkiewicz ◽  
...  
Keyword(s):  
Gene Fusion ◽  
Large Cell ◽  
Distinct Subgroup ◽  
Fusion Transcripts

PAX3-FKHR and PAX7-FKHR Gene Fusions Are Prognostic Indicators in Alveolar Rhabdomyosarcoma: A Report From the Children’s Oncology Group

2002 ◽  
Vol 20 (11) ◽  
pp. 2672-2679 ◽  
Author(s):  
Poul H.B. Sorensen ◽  
James C. Lynch ◽  
Stephen J. Qualman ◽  
Roberto Tirabosco ◽  
Jerian F. Lim ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Metastatic Disease ◽  
Gene Fusion ◽  
Bone Marrow Involvement ◽  
Alveolar Rhabdomyosarcoma ◽  
Outcome Analysis ◽  
Striking Difference ◽  
Gene Fusions ◽  
Fusion Transcripts ◽  
Increased Risk

PURPOSE: Alveolar rhabdomyosarcoma (ARMS) is an aggressive soft tissue malignancy of children and adolescents. Most ARMS patients express PAX3-FKHR or PAX7-FKHR gene fusions resulting from t(2;13) or t(1;13) translocations, respectively. We wished to confirm the diagnostic specificity of gene fusion detection in a large cohort of RMS patients and to evaluate whether these alterations influence clinical outcome in ARMS. PATIENTS AND METHODS: We determined PAX3-FKHR or PAX7-FKHR fusion status in 171 childhood rhabdomyosarcoma (RMS) patients entered onto the Intergroup Rhabdomyosarcoma Study IV, including 78 ARMS patients, using established reverse transcriptase polymerase chain reaction assays. All patients received central pathologic review and were treated using uniform protocols, allowing for meaningful outcome analysis. We examined the relationship between gene fusion status and clinical outcome in the ARMS cohort. RESULTS: PAX3-FKHR and PAX7-FKHR fusion transcripts were detected in 55% and 22% of ARMS patients, respectively; 23% were fusion-negative. All other RMS patients lacked transcripts, confirming the specificity of these alterations for ARMS. Fusion status was not associated with outcome differences in patients with locoregional ARMS. However, in patients presenting with metastatic disease, there was a striking difference in outcome between PAX7-FKHR and PAX3-FKHR patient groups (estimated 4-year overall survival rate of 75% for PAX7-FKHR v 8% for PAX3-FKHR; P = .0015). Multivariate analysis demonstrated a significantly increased risk of failure (P = .025) and death (P = .019) in patients with metastatic disease if their tumors expressed PAX3-FKHR. Among metastatic ARMS, bone marrow involvement was significantly higher in PAX3-FKHR–positive patients. CONCLUSION: Not only are PAX-FKHR fusion transcripts specific for ARMS, but expression of PAX3-FKHR and PAX7-FKHR identifies a very high-risk subgroup and a favorable outcome subgroup, respectively, among patients presenting with metastatic ARMS.

Abstract 5267: Gene fusion database to create custom panels: Enabling detection of fusion transcripts and gene expression assays

2016 ◽  
Author(s):  
Efren Ballesteros-Villagrana ◽  
Jeoffrey Schageman ◽  
Kelli Bramlett ◽  
Paul Williams ◽  
Scott Myrand ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Fusion ◽  
Fusion Transcripts

The Heterogeneous Fusion Gene Landscape in Pediatric Acute Lymphoblastic Leukemia

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4081-4081
Author(s):  
Yanara Marincevic-Zuniga ◽  
Johan Dahlberg ◽  
Sara Nilsson ◽  
Amanda Raine ◽  
Jonas Abrahamsson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Acute Lymphoblastic Leukemia ◽  
Gene Fusion ◽  
Conflicts Of Interest ◽  
Fusion Gene ◽  
Lymphoblastic Leukemia ◽  
Fusion Genes ◽  
Fusion Transcripts ◽  
Pediatric All

Abstract Background: Next generation sequencing allows for the detection of expressed fusion transcripts across the transcriptome and has spurred the discovery of many novel chimeric transcripts in various cancers. Structural chromosomal rearrangements that lead to fusion transcripts are a hallmark of acute lymphoblastic leukemia (ALL) and serve as markers for diagnosis and stratification of pediatric ALL patients into prognostically relevant subgroups. Improved delineation of structural alterations in ALL could provide additional information for prognosis in ALL and for improved stratification of patients into treatment groups. Methods: To identify novel fusion transcripts in primary pediatric ALL cells we performed whole transcriptome sequencing of 134 BCP and T-ALL patient samples collected at diagnosis. Our study include samples from patients with the well-known ALL subtypes t(12;21)ETV6-RUNX1, high hyperdiploid (51-67 chromosomes), t(9;22)BCR-ABL1, 11q23/MLL and dic(9;20), in addition to patients with undefined karyotype or non-recurrent cytogenetic aberrations ("undefined" and "other") (n=58). FusionCatcher was used for the detection of somatic fusion genes, followed by a stringent filtering pipeline including gene fusion validation by Sanger sequencing in order to reduce the number of false positives. Principal component analysis (PCA) of patients with fusion genes was performed using genome wide gene expression levels and DNA methylation levels (Infinium HumanMethylation450 bead array). Results: We identified and validated 60 unique fusion events in almost half of the analyzed patients (n=69). Of the identified fusion genes, 60% have not previously been reported in ALL or other forms of cancer. The majority of the fusion genes were found in a single patient, but 23% were recurrent, including known ALL fusion genes (n=10) and novel fusion genes (n=7). We found that BCP-ALL samples displayed a higher number of validated fusion genes (54%) compared to the T-ALL samples (28%) moreover in BCP-ALL patients with "other" and "undefined" karyotypes, we detected fusion genes in 71% and 61% of the samples, respectively. High hyperdiploid patients had the lowest rate of validated fusion genes (24%) compared to the other well-known subtypes, where we detected subtype-associated fusion genes in 97% of cases. We also identified promiscuous fusion gene partners, such as ETV6, RUNX1, PAX5 and ZNF384 that fused with up to five different genes. Interestingly, PCA revealed molecularly distinct gene expression and DNA methylation signatures associated with these fusion partners. Conclusion: RNA-sequencing of pediatric ALL cells revealed a detailed view of the heterogeneous fusion gene landscape, identifying both known and novel fusion genes. By grouping samples based on recurrent gene fusion partners we are able to find shared gene expression and DNA methylation patterns compared to other subtypes of ALL, suggesting a shared molecular etiology within these distinct subgroups, offering novel insights into the delineation of fusion genes in ALL. Disclosures No relevant conflicts of interest to declare.

scholarly journals Identification of gene fusion transcripts by transcriptome sequencing in BRCA1-mutated breast cancers and cell lines

2011 ◽  
Vol 4 (1) ◽  
Author(s):  
Kevin CH Ha ◽  
Emilie Lalonde ◽  
Lili Li ◽  
Luca Cavallone ◽  
Rachael Natrajan ◽  
...  
Keyword(s):  
Cell Lines ◽  
Transcriptome Sequencing ◽  
Gene Fusion ◽  
Breast Cancers ◽  
Fusion Transcripts

Detection of gene fusion transcripts in Peripheral T-cell Lymphoma using a multiplexed targeted sequencing assay

2021 ◽  
Author(s):  
Fanny Drieux ◽  
Philippe Ruminy ◽  
Vincent Sater ◽  
Vinciane Marchand ◽  
Virginie Fataccioli ◽  
...  
Keyword(s):  
T Cell ◽  
Gene Fusion ◽  
Cell Lymphoma ◽  
T Cell Lymphoma ◽  
Targeted Sequencing ◽  
Peripheral T Cell Lymphoma ◽  
Fusion Transcripts

Poly-gene fusion transcripts and chromothripsis in prostate cancer

2012 ◽  
Vol 51 (12) ◽  
pp. 1144-1153 ◽  
Author(s):  
Chunxiao Wu ◽  
Alexander W. Wyatt ◽  
Andrew McPherson ◽  
Dong Lin ◽  
Brian J. McConeghy ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Gene Fusion ◽  
Fusion Transcripts

scholarly journals Fusion FISH Imaging: Single-Molecule Detection of Gene Fusion Transcripts In Situ

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e93488 ◽  
Author(s):  
Fatu Badiane Markey ◽  
William Ruezinsky ◽  
Sanjay Tyagi ◽  
Mona Batish
Keyword(s):  
Single Molecule ◽  
Gene Fusion ◽  
Single Molecule Detection ◽  
Fusion Transcripts

scholarly journals A Deep Learning Approach to the Screening of Oncogenic Gene Fusions in Humans

2019 ◽  
Vol 20 (7) ◽  
pp. 1645 ◽  
Author(s):  
Marta Lovino ◽  
Gianvito Urgese ◽  
Enrico Macii ◽  
Santa Di Cataldo ◽  
Elisa Ficarra
Keyword(s):  
Deep Learning ◽  
Gene Fusion ◽  
Research Problem ◽  
Protein Domain ◽  
Gene Fusions ◽  
Protein Fusion ◽  
Large Dataset ◽  
Oncogenic Potential ◽  
Fusion Transcripts ◽  
Oncogenic Gene

Gene fusions have a very important role in the study of cancer development. In this regard, predicting the probability of protein fusion transcripts of developing into a cancer is a very challenging and yet not fully explored research problem. To this date, all the available approaches in literature try to explain the oncogenic potential of gene fusions based on protein domain analysis, that is cancer-specific and not easy to adapt to newly developed information. In our work, we choose the raw protein sequences as the input baseline, and propose the use of deep learning, and more specifically Convolutional Neural Networks, to infer the oncogenity probability score of gene fusion transcripts and to group them into a number of categories (e.g., oncogenic/not oncogenic). This is an inherently flexible methodology that, unlike previous approaches, can be re-trained with very less efforts on newly available data (for example, from a different cancer). Based on experimental results on a large dataset of pre-annotated gene fusions, our method is able to predict the oncogenity potential of gene fusion transcripts with accuracy of about 72%, which increases to 86% if we consider the only instances that are classified with a high confidence level.

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